{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e092b82b-28c4-400c-92c2-783464988de9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train.shape= (60000, 28, 28)\n",
      "y_train.shape= (60000,)\n",
      "x_test.shape= (10000, 28, 28)\n",
      "y_test.shape= (10000,)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">160</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │         <span style=\"color: #00af00; text-decoration-color: #00af00\">4,640</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25088</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │     <span style=\"color: #00af00; text-decoration-color: #00af00\">3,211,392</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,290</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m16\u001b[0m)     │           \u001b[38;5;34m160\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m16\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m16\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │         \u001b[38;5;34m4,640\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_3 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_4 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten_1 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25088\u001b[0m)          │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │     \u001b[38;5;34m3,211,392\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_5 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m1,290\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3,217,482</span> (12.27 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m3,217,482\u001b[0m (12.27 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3,217,482</span> (12.27 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m3,217,482\u001b[0m (12.27 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m37s\u001b[0m 47ms/step - loss: 0.1682 - sparse_categorical_accuracy: 0.9483 - val_loss: 0.0586 - val_sparse_categorical_accuracy: 0.9826\n",
      "Epoch 2/10\n",
      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 48ms/step - loss: 0.0581 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.0537 - val_sparse_categorical_accuracy: 0.9826\n",
      "Epoch 3/10\n",
      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m37s\u001b[0m 49ms/step - loss: 0.0404 - sparse_categorical_accuracy: 0.9870 - val_loss: 0.0365 - val_sparse_categorical_accuracy: 0.9900\n",
      "Epoch 4/10\n",
      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 48ms/step - loss: 0.0340 - sparse_categorical_accuracy: 0.9888 - val_loss: 0.0372 - val_sparse_categorical_accuracy: 0.9899\n",
      "Epoch 5/10\n",
      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 48ms/step - loss: 0.0257 - sparse_categorical_accuracy: 0.9915 - val_loss: 0.0310 - val_sparse_categorical_accuracy: 0.9908\n",
      "Epoch 6/10\n",
      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 48ms/step - loss: 0.0226 - sparse_categorical_accuracy: 0.9930 - val_loss: 0.0321 - val_sparse_categorical_accuracy: 0.9910\n",
      "Epoch 7/10\n",
      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 48ms/step - loss: 0.0188 - sparse_categorical_accuracy: 0.9937 - val_loss: 0.0357 - val_sparse_categorical_accuracy: 0.9896\n",
      "Epoch 8/10\n",
      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m35s\u001b[0m 47ms/step - loss: 0.0172 - sparse_categorical_accuracy: 0.9941 - val_loss: 0.0403 - val_sparse_categorical_accuracy: 0.9899\n",
      "Epoch 9/10\n",
      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m35s\u001b[0m 47ms/step - loss: 0.0169 - sparse_categorical_accuracy: 0.9942 - val_loss: 0.0355 - val_sparse_categorical_accuracy: 0.9911\n",
      "Epoch 10/10\n",
      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 48ms/step - loss: 0.0138 - sparse_categorical_accuracy: 0.9951 - val_loss: 0.0373 - val_sparse_categorical_accuracy: 0.9910\n",
      "157/157 - 2s - 10ms/step - loss: 0.0317 - sparse_categorical_accuracy: 0.9905\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "mnist=tf.keras.datasets.mnist\n",
    "(x_train,y_train),(x_test,y_test)=mnist.load_data()\n",
    "x_train,x_test=tf.cast(x_train,dtype=tf.float32)/255.0,tf.cast(x_test,dtype=tf.float32)/255.0\n",
    "y_train,y_test=tf.cast(y_train,dtype=tf.int32),tf.cast(y_test,dtype=tf.int32)\n",
    "print(\"x_train.shape=\",x_train.shape)\n",
    "print(\"y_train.shape=\",y_train.shape)\n",
    "print(\"x_test.shape=\",x_test.shape)\n",
    "print(\"y_test.shape=\",y_test.shape)\n",
    "\n",
    "\n",
    "model=tf.keras.models.Sequential([\n",
    "    tf.keras.layers.Conv2D(16,kernel_size=(3,3),padding='same',activation=tf.nn.relu,input_shape=(28,28,1)),\n",
    "    tf.keras.layers.MaxPool2D(pool_size=(2,2),strides=(1,1),padding='same'),\n",
    "    tf.keras.layers.Dropout(0.2),\n",
    "    tf.keras.layers.Conv2D(32,kernel_size=(3,3),padding='same',activation=tf.nn.relu),\n",
    "    tf.keras.layers.MaxPool2D(pool_size=(2,2),strides=(1,1),padding='same'),\n",
    "    tf.keras.layers.Dropout(0.2),\n",
    "    tf.keras.layers.Flatten(),\n",
    "    tf.keras.layers.Dense(128,activation='relu'),\n",
    "    tf.keras.layers.Dropout(0.2),\n",
    "    tf.keras.layers.Dense(10,activation='softmax')\n",
    "])\n",
    "model.summary()\n",
    "\n",
    "\n",
    "model.compile(optimizer='adam',\n",
    "             loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),\n",
    "             metrics=['sparse_categorical_accuracy'])\n",
    "history=model.fit(x_train,y_train,batch_size=64,epochs=10,validation_split=0.2)                                                       \n",
    "model.evaluate(x_test,y_test,batch_size=64,verbose=2)\n",
    "\n",
    "\n",
    "loss=history.history['loss']\n",
    "acc=history.history['sparse_categorical_accuracy']\n",
    "val_loss=history.history['val_loss']\n",
    "val_acc=history.history['val_sparse_categorical_accuracy']\n",
    "plt.figure(figsize=(10,3))\n",
    "plt.subplot(121)\n",
    "plt.plot(loss,color='b',label='train')\n",
    "plt.plot(val_loss,color='r',label='test')\n",
    "plt.ylabel('loss')\n",
    "plt.legend()\n",
    "plt.subplot(122)\n",
    "plt.plot(acc,color='b',label='train')\n",
    "plt.plot(val_loss,color='r',label='test')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "for i in range(10):\n",
    "    t=np.random.randint(1,10000)\n",
    "    x=tf.reshape(x_test[t],(1,28,28))\n",
    "    y_pred=np.argmax(model.predict(x),axis=1)\n",
    "    plt.subplot(2,5,i+1)\n",
    "    plt.axis('off')\n",
    "    plt.rcParams['font.sans-serif']=['SimHei']\n",
    "    plt.imshow(x_test[t],cmap='gray')\n",
    "    title='标签值:'+str((y_test.numpy())[t])+'\\n预测值:'+str(y_pred)\n",
    "    plt.title(title)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc3345bf-4b6f-411e-9ec0-49deeff63184",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:base] *",
   "language": "python",
   "name": "conda-base-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
